A search engine returns search results that match a search query submitted by a user. Typically, the search engine orders the search results. The order usually takes the form of a “ranking”, where the document with the highest ranking is the document considered most likely to satisfy the interest reflected in the search criteria specified by the user. Once the matching documents have been determined, and the display order of those documents has been determined, the search engine sends to the user that issued the search a “search engine results page” that presents information about the matching documents in the display order. Typically, the number of documents that match a search is so large that the user is presented with a search engine results page that only displays information about the top N ranking documents, where N may be significantly smaller than the total number of matching documents. The search engine results page typically includes a control that allows the user to retrieve information about the “next N” matching documents, in case the first N matching documents do not entirely satisfy the user's interest.
Thus, search results are typically generic in that the same search results are sent to all users. Unfortunately, the search results that have the highest ranking may not correlate well with the search results in which a particular user is interested. Thus, a user might have to wade through many pages of search results to locate results of interest. Worse yet, the search results in which a particular user is interested might have such a low ranking that the user does not find them at all.
An alternative to this technique is “taxonomy-based” searching. Briefly, a taxonomy describes categories and relationships between categories. Typically, each document (e.g., search result) is placed into a taxonomy category, which can improve the search result quality.
One type of taxonomy based searching is based on documents from a local database. Taxonomies based on local databases are sometimes specific to particular subject matter. For example, in a medical taxonomy the categories and relationships between categories reflect medical subject matter. As a particular example, a pharmaceutical company might develop a taxonomy for documents in its own databases. However, because the taxonomy only categorizes documents from the local databases, more general search results from the World Wide Web are not included.
Another technique might allow for categorizing search results from the World Wide Web into a taxonomy. However, this technique typically imposes a single general taxonomy upon all users. For example, a search engine might organize search results into a single general taxonomy that applies to all search queries. As a consequence, user's specific needs, such as medical related searches or stock market related searches do not benefit from the general taxonomy.
In view of the foregoing, improved techniques are needed for organizing search results are presenting search engine result pages to a user.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.